SoroushVahidi/scidocs huggingface dataset:
Out-of-domain FrameNet Semantic Role Labeling
(2017)

Domain dependence of NLP systems is one of the major obstacles to their application in large-scale text analysis, also restricting the applicability of FrameNet semantic role labeling (SRL) systems. Yet, current FrameNet SRL systems are still only evaluated on a single in-domain test set. For the first time, we study the domain dependence of FrameNet SRL on a wide range of benchmark sets. We create a novel test set for FrameNet SRL based on user-generated web text and find that the major bottleneck for out-of-domain FrameNet SRL is the frame identification step. To address this problem, we develop a simple, yet efficient system based on distributed word representations. Our system closely approaches the state-of-the-art in-domain while outperforming the best available frame identification system out-of-domain. We publish our system and test data for research purposes.1
S-Match: an algorithm and an implementation of semantic matching
(2004)

We think of Match as an operator which takes two graph-like structures (e.g., conceptual hierarchies or ontologies) and produces a mapping between those nodes of the two graphs that correspond semantically to each other. Semantic matching is a novel approach where semantic correspondences are discovered by computing, and returning as a result, the semantic information implicitly or explicitly codified in the labels of nodes and arcs. In this paper we present an algorithm implementing semantic matching, and we discuss its implementation within the S-Match system. We also test S-Match against three state of the art matching systems. The results, though preliminary, look promising, in particular for what concerns precision and recall.
Behavioral Experiments for Assessing the Abstract Argumentation Semantics of Reinstatement
(2010)

Argumentation is a very fertile area of research in Artificial Intelligence, and various semantics have been developed to predict when an argument can be accepted, depending on the abstract structure of its defeaters and defenders. When these semantics make conflicting predictions, theoretical arbitration typically relies on ad hoc examples and normative intuition about what prediction ought to be the correct one. We advocate a complementary, descriptive-experimental method, based on the collection of behavioral data about the way human reasoners handle these critical cases. We report two studies applying this method to the case of reinstatement (both in its simple and floating forms). Results speak for the cognitive plausibility of reinstatement and yet show that it does not yield the full expected recovery of the attacked argument. Furthermore, results show that floating reinstatement yields comparable effects to that of simple reinstatement, thus arguing in favor of preferred argumentation semantics, rather than grounded argumentation semantics. Besides their theoretical value for validating and inspiring argumentation semantics, these results have applied value for developing artificial agents meant to argue with human users.
An integrated framework for human activity recognition
(2012)

This poster presents an integrated framework to enable using standard non-sequential machine learning tools for accurate multi-modal activity recognition. Our framework contains simple pre- and post-classification strategies such as class-imbalance correction on the learning data using structure preserving oversampling, leveraging the sequential nature of sensory data using smoothing of the predicted label sequence and classifier fusion, respectively, for improved performance. Through evaluation on recent publicly-available OPPORTUNITY activity datasets comprising of a large amount of multi-dimensional, continuous-valued sensory data, we show that our proposed strategies are effective in improving the performance over common techniques such as One Nearest Neighbor (1NN) and Support Vector Machines (SVM). Our framework also shows better performance over sequential probabilistic models, such as Conditional Random Field (CRF) and Hidden Markov Models (HMM) and when these models are used as meta-learners.
The five-factor model of personality and managerial performance: validity gains through the use of 360 degree performance ratings.
(2009)

This study investigated the usefulness of the five-factor model (FFM) of personality in predicting two aspects of managerial performance (task vs. contextual) assessed by utilizing the 360 degree performance rating system. The authors speculated that one reason for the low validity of the FFM might be the failure of single-source (e.g., supervisor) ratings to comprehensively capture the construct of managerial performance. The operational validity of personality was found to increase substantially (50%-74%) across all of the FFM personality traits when both peer and subordinate ratings were added to supervisor ratings according to the multitrait-multimethod approach. Furthermore, the authors responded to the recent calls to validate tests via a multivariate (e.g., multitrait-multimethod) approach by decomposing overall managerial performance into task and contextual performance criteria and by using multiple rating perspectives (sources). Overall, this study contributes to the evidence that personality may be even more useful in predicting managerial performance if the performance criteria are less deficient.
A bias for posterior α-band power suppression versus enhancement during shifting versus maintenance of spatial attention
(2009)

Voluntarily directing visual attention to a cued position in space leads to improved processing of forthcoming visual stimuli at the attended position, and attenuated processing of competing stimuli elsewhere, due to anticipatory tuning of visual cortex activity. In EEG, recent evidence points to a determining role of modulations of posterior alpha-band activity (8-14 Hz) in such anticipatory facilitation (alpha-power decreases) versus inhibition (alpha-power increases). Yet, while such alpha-modulations are a common finding, the direction of modulation varies to a great extent across studies implying dependence on task demands. Here, we reveal opposite modulation of posterior alpha-power with early/initiation versus later/sustained processes of anticipatory attention orienting. Marked alpha-decreases were observed during shifting of attention (initial 700 ms) over occipito-parietal areas processing to-be-attended visual space, while alpha-increases dominated in the subsequent maintenance phase (>700 ms) over occipito-parietal cortex tuned to unattended positions. Notably, the presence of alpha-modulation strongly depended on individual resting alpha-power. Overall, this provides further support to an active facilitative versus inhibitory role of alpha-power decreases and increases and suggests that these attention-related changes are differentially deployed during anticipatory attention orienting to prepare versus maintain the cortex for optimal target processing.
A Novel Method for Accurate and Efficient Barcode Detection with Morphological Operations
(2012)

Barcode technology is the pillar of automatic identification, that is used in a wide range of real-time applications with various types of codes. The different types of codes and applications impose special problems, so there is a continuous need for solutions with improved effectiveness. There are several methods for barcode localization, that are well characterized by accuracy and speed. Particularly, high-speed processing places need automatic barcode localization, e.g. conveyor belts, automated production, where missed detections cause loss of profit. In this paper, we mainly deal with segmentation of images with 1D barcode, but also analyze the operation of different methods for 2D barcode images as well. Our goal is to detect automatically, rapidly and accurately the barcode location by the help of extracted features. We compare some published method from the literature, which basically rely on the contrast between the background and the shape that represent the code. We also propose a novel algorithm, that outperforms the others in both accuracy and efficiency in detecting 1D codes.
A Two-Layered Permission-Based Android Malware Detection Scheme
(2014)

Android platform has become the main target of the malware developers in the past few years. One of Android's main defense mechanisms against malicious apps is a permission-based access control mechanism. It is a feasible approach to detect a potential malicious application based on the permissions it requested. In this paper, we proposed a two-layered permission based detection scheme for detecting malicious Android applications. Comparing with previous researches, we consider the apps requested permission pairs as the additional condition, and we also consider used permissions to improve detection accuracy. The result of an evaluation, performed using 28548 benign apps and 1536 malicious apps, indicates that a two-layered permission-based detector has high detection rate of malware.
A signal processing approach to fair surface design
(1995)

In this paper we describe a new tool for interactive free-form fair surface design. By generalizing classical discrete Fourier analysis to two-dimensional discrete surface signals – functions defined on polyhedral surfaces of arbitrary topology –, we reduce the problem of surface smoothing, or fairing, to low-pass filtering. We describe a very simple surface signal low-pass filter algorithm that applies to surfaces of arbitrary topology. As opposed to other existing optimization-based fairing methods, which are computationally more expensive, this is a linear time and space complexity algorithm. With this algorithm, fairing very large surfaces, such as those obtained from volumetric medical data, becomes affordable. By combining this algorithm with surface subdivision methods we obtain a very effective fair surface design technique. We then extend the analysis, and modify the algorithm accordingly, to accommodate different types of constraints. Some constraints can be imposed without any modification of the algorithm, while others require the solution of a small associated linear system of equations. In particular, vertex location constraints, vertex normal constraints, and surface normal discontinuities across curves embedded in the surface, can be imposed with this technique. CR
Filling Holes in Triangular Meshes in Engineering
(2012)

In this paper, a novel hole-filling algorithm for triangular meshes is proposed. Firstly, the hole is triangulated into a set of new triangles using modified principle of minimum angle. Then the initial patching mesh is refined according to the density of vertices on boundary edges. Finally, the patching mesh is optimized via the bilateral filter to recover missed features. Experimental results demonstrate that the proposed algorithm fills complex holes robustly, and preserves geometric features to a certain extent as well. The resulted meshes are of good quality for engineering.
Polygon mesh repairing: An application perspective
(2013)

Nowadays, digital 3D models are in widespread and ubiquitous use, and each specific application dealing with 3D geometry has its own quality requirements that restrict the class of acceptable and supported models. This article analyzes typical defects that make a 3D model unsuitable for key application contexts, and surveys existing algorithms that process, repair, and improve its structure, geometry, and topology to make it appropriate to case-by-case requirements.
 The analysis is focused on polygon meshes, which constitute by far the most common 3D object representation. In particular, this article provides a structured overview of mesh repairing techniques from the point of view of the application context. Different types of mesh defects are classified according to the upstream application that produced the mesh, whereas mesh quality requirements are grouped by representative sets of downstream applications where the mesh is to be used. The numerous mesh repair methods that have been proposed during the last two decades are analyzed and classified in terms of their capabilities, properties, and guarantees. Based on these classifications, guidelines can be derived to support the identification of repairing algorithms best-suited to bridge the compatibility gap between the quality provided by the upstream process and the quality required by the downstream applications in a given geometry processing scenario.
An Integrated Approach to Filling Holes in Meshes
(2010)

Data obtained by scanning 3D models typically contains missing pieces and holes. This paper describes an integrated approach for filling holes in triangular meshes obtained during the process of surface reconstruction, which consists of three steps: triangulation, refinement and smoothing. The approach first generates a minimal triangulation to the hole, and then iteratively subdivides the patching meshes making it assort with the density of surrounding meshes. Finally, geometric optimization is enforced to the patching meshes for improving regularity. The approach can deal with arbitrary holes in oriented connected manifold meshes. Experimental results on several triangular meshes are presented.
Filling Holes in Complex Surfaces using Volumetric Diffusion
(2002)

We address the problem of building watertight 3D models from surfaces that contain holes − for example, sets of range scans that observe most but not all of a surface. We specifically address situations in which the holes are too geometrically and topologically complex to fill using triangulation algorithms. Our solution begins by constructing a signed distance function, the zero set of which defines the surface. Initially, this function is defined only in the vicinity of observed surfaces. We then apply a diffusion process to extend this function through the volume until its zero set bridges whatever holes may be present. If additional information is available, such as known-empty regions of space inferred from the lines of sight to a 3D scanner, it can be incorporated into the diffusion process. Our algorithm is simple to implement, is guaranteed to produce manifold noninterpenetrating surfaces, and is efficient to run on large datasets because computation is limited to areas near holes.
Role-Based Access Control Model Supporting Regional Division in Smart Grid System
(2013)

Smart Grid is a modern electric power infrastructure that incorporates elements of traditional power systems and information and communication technology (ICT) with the aim to improve the reliability, efficiency and safety as crucial requirements of critical infrastructure systems. Relying on the ICT, Smart Grid exposes electric power systems to new security issues and vulnerabilities over the network. Therefore, the security is becoming an increasing concern at both physical and information levels. Access controls are one of the most important aspects of information security. Role-based access control (RBAC) model is widely used in complex systems that are characterized by many participants. However, the existing models are not suitable for critical infrastructure systems with specialized features, such as a high number of devices dispersed over the vast geographical regions in “an Internetlike distributed environment”. In this paper, access control mechanism optimized for Smart Grid systems is developed considering the regional division and concept of areas of responsibility (AOR). To this end, the standardized RBAC model will be extended to provide an efficient and consistent policy with greater level of granularity from the aspect of resource protection. The proposed RBACAOR model is formally described and then applied to the smart grid environment.
Polyphenolic content and antioxidant properties of Moringa oleifera leaf extracts and enzymatic activity of liver from goats supplemented with Moringa oleifera leaves/sunflower seed cake.
(2012)

The study investigated antioxidant potency of Moringa oleifera leaves in different in vitro systems using standard phytochemical methods. The antioxidative effect on the activities of superoxide dismutase (SOD), catalase (CAT), lipid peroxidation (LPO) and reduced glutathione (GSH) were investigated in goats supplemented with M. oleifera (MOL) or sunflower seed cake (SC). The acetone extract had higher concentrations of total flavonoids (295.01 ± 1.89 QE/g) followed by flavonols (132.74 ± 0.83 QE/g), phenolics (120.33 ± 0.76 TE/g) and then proanthocyanidins (32.59 ± 0.50 CE/g) than the aqueous extract. The reducing power of both solvent extracts showed strong antioxidant activity in a concentration dependent manner. The acetone extract depicted higher percentage inhibition against DPPH, ABTS and nitric oxide radicals which were comparable with reference standard antioxidants (vitamin C and BHT). MOL increased the antioxidant activity of GSH (186%), SOD (97.8%) and catalase (0.177%). Lipid peroxidation was significantly reduced by MOL. The present study suggests that M. oleifera could be a potential source of compounds with strong antioxidant potential.
A 10-GS/s 4-Bit Single-Core Digital-to-Analog Converter for Cognitive Ultrawidebands
(2017)

This brief delineates the design and realization of a 10-GS/s 4-bit digital-to-analog converter (DAC) for the cognitive ultrawideband (CUWB), an emerging solution for low interference and efficient spectrum utilization in communication networks. The DAC serves as the data converter for the adaptive waveform transmitter therein, largely to reduce its power dissipation and hardware complexity. For reasons of low power dissipation and low-cost CUWB application, the resolution of the DAC is 4 bits, its realization is in standard 65-nm CMOS, and the architecture is a single core. The binary current-steering DAC includes critical building blocks such as current sources and a novel deglitcher circuit. The current sources are designed for small area with high linearity based on our derived relationship between current-source output resistance and linearity parameters [integral nonlinearity (INL) and spurious-free dynamic range (SFDR)]. The deglitcher design includes high-speed source followers as high-speed low voltage swing buffers to improve the linearity by decreasing the output glitch energy. The DAC embodies an in situ hardware efficient (small integrated-circuit area and reduced input/output pinout) tester that generates 4 × 10-Gb/s test-data pattern to facilitate functional verification. The designed DAC achieves ≤ 0.16-least significant bit INL/differential nonlinearity and > 23-dBc SFDR over the Nyquist bandwidth up to 4.53 GHz, and features the most competitive figures-of-merit of all similar DACs reported to date.
Inferring causal impact using Bayesian structural time-series models
(2013)

An important problem in econometrics and marketing is to infer the causal impact that a designed market intervention has exerted on an outcome metric over time. In order to allocate a given budget optimally, for example, an advertiser must determine the incremental contributions that different advertising campaigns have made to web searches, product installs, or sales. This paper proposes to infer causal impact on the basis of a diffusion-regression state-space model that predicts the counterfactual market response that would have occurred had no intervention taken place. In contrast to classical difference-in-differences schemes, state-space models make it possible to (i) infer the temporal evolution of attributable impact, (ii) incorporate empirical priors on the parameters in a fully Bayesian treatment, and (iii) flexibly accommodate multiple sources of variation, including the time-varying influence of contemporaneous covariates, i.e., synthetic controls. Using a Markov chain Monte Carlo algorithm for posterior inference, we illustrate the statistical properties of our approach on synthetic data. We then demonstrate its practical utility by evaluating the effect of an online advertising campaign on search-related site visits. We discuss the strengths and limitations of our approach in improving the accuracy of causal attribution, power analyses, and principled budget allocation.
Understanding the influence of dead-time on GaN based synchronous boost converter
(2014)

Gallium Nitride (GaN) based power switching devices are known to be superior to conventional Si devices due to properties such as low loss and fast switching speed. However, as a new technology, some special characteristics of GaN-based power devices still remain unknown to the public. This paper tries to investigate the effect of dead-time on a GaN-based synchronous boost converter, as compared to its Si counterpart. It is found out that GaN-based converter is more sensitive to dead-time related loss as a result of fast switching transition and high reverse voltage drop. Improper selection of dead-time value can offset its advantage over Si converter. Analyses also show that GaN HEMTs have different switching characteristics compared to Si MOSFET due to low Cgd to Cds ratio and lack of reverse recovery. These critical findings will help power electronic engineers take better advantage of GaN technology in synchronous rectification and inverter applications.
Clonogenic assay of cells in vitro
(2006)

Clonogenic assay or colony formation assay is an in vitro cell survival assay based on the ability of a single cell to grow into a colony. The colony is defined to consist of at least 50 cells. The assay essentially tests every cell in the population for its ability to undergo “unlimited” division. Clonogenic assay is the method of choice to determine cell reproductive death after treatment with ionizing radiation, but can also be used to determine the effectiveness of other cytotoxic agents. Only a fraction of seeded cells retains the capacity to produce colonies. Before or after treatment, cells are seeded out in appropriate dilutions to form colonies in 1–3 weeks. Colonies are fixed with glutaraldehyde (6.0% v/v), stained with crystal violet (0.5% w/v) and counted using a stereomicroscope. A method for the analysis of radiation dose–survival curves is included.
Animations 25 Years Later: New Roles and Opportunities
(2016)

Animations are commonplace in today's user interfaces. From bouncing icons that catch attention, to transitions helping with orientation, to tutorials, animations can serve numerous purposes. We revisit Baecker and Small's pioneering work Animation at the Interface, 25 years later. We reviewed academic publications and commercial systems, and interviewed 20 professionals of various backgrounds. Our insights led to an expanded set of roles played by animation in interfaces today for keeping in context, teaching, improving user experience, data encoding and visual discourse. We illustrate each role with examples from practice and research, discuss evaluation methods and point to opportunities for future research. This expanded description of roles aims at inspiring the HCI research community to find novel uses of animation, guide them towards evaluation and spark further research.